| Description |
1 online resource (272 p.) |
| Series |
Computational Intelligence Techniques Ser |
|
Computational Intelligence Techniques Ser
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Editors -- Contributors -- Preface -- Chapter 1 Graph of Words Model for Natural Language Processing -- 1.1 Introduction -- 1.1.1 Lexical and Morphological Analysis -- 1.1.2 Syntactic Analysis -- 1.1.3 Semantic Analysis -- 1.1.4 Discourse Integration -- 1.1.5 Pragmatic Analysis -- 1.2 Machine Learning and Text Modelling -- 1.3 BoW Model -- 1.3.1 Introduction -- 1.3.1.1 Step 1: Collect the Data -- 1.3.1.2 Step 2: Vocabulary Design -- 1.3.1.3 Step 3: Document Vectors Creation -- 1.3.1.4 Scoring Words |
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1.3.2 Limitations of the BoW Model -- 1.4 Graph of Words (GoW) Model -- 1.4.1 Basic Terminology of Graphs -- 1.4.1.1 Real-world Graphs -- 1.4.1.2 Graphs in Linguistics -- 1.4.2 Semantic Similarity and Ambiguity -- 1.4.3 How to Build a GoW -- 1.4.3.1 Preliminary Concepts -- 1.4.4 Construction of a GoW -- 1.4.5 Use of GoW in Text Mining -- 1.4.6 GoW Mining -- 1.4.6.1 Graph Degeneracy -- 1.4.6.2 K-core Decomposition -- 1.4.6.3 K-truss -- 1.5 Discussion and Future Scope -- References -- Chapter 2 Application of NLP Using Graph Approaches -- 2.1 Introduction -- 2.1.1 What Is a Graph? |
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2.2 Graph Embeddings -- 2.3 Dynamic Graph of Words -- 2.4 Cross-lingual and Multilingual Graphical Approaches -- 2.5 Topological Analysis of Graphs -- 2.6 Adversarial Networks for Natural Language Processing -- 2.7 Heterogeneous Information Networks for Textual Information -- 2.8 Summary of Ontology and Knowledge Graphs -- 2.9 Topic Identification -- 2.10 Major Processes of NLP Using Graphical Approaches and Their Applications in the Real World -- 2.10.1 Summarization -- 2.10.1.1 News -- 2.10.1.2 Assignments and E-learning -- 2.10.1.3 Summarization of Financial or Legal Documents |
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2.10.2 Semi-supervised Passage Retrieval -- 2.10.3 Keyword Extraction -- 2.10.3.1 The Steps of the TextRank Algorithm -- 2.10.4 Information Extraction -- 2.10.5 Question Answering -- 2.10.6 Cross-language Information Retrieval -- 2.10.7 Term Weighting -- 2.10.8 Topic Segmentation -- 2.10.8.1 Graph-based Topic Segmentation -- 2.10.9 Machine Translation -- 2.10.9.1 Graph-based Machine Translation -- 2.10.10 Discourse Analysis -- 2.11 Conclusion and Future Scope of NLP -- 2.12 Datasets for NLP Applications -- References |
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Chapter 3 Graph-based Extractive Approach for English and Hindi Text Summarization -- 3.1 Introduction -- 3.2 Text Summarization Approaches -- 3.2.1 Text Summarization Based on Number of Documents -- 3.2.2 Text Summarization Based on the Summary's Purpose -- 3.2.3 Text Summarization Techniques -- 3.2.4 Text Summarization Based on Level of Language -- 3.2.5 Text Summarization Based on Output Style -- 3.2.6 Text Summarization Based on the Summary's Characteristics -- 3.3 Literature Survey -- 3.4 Graph-based Algorithms -- 3.4.1 PageRank Algorithm -- 3.4.2 Text Rank Algorithm -- 3.5 TF-IDF Algorithm |
| Notes |
Description based upon print version of record |
|
3.6 Methodology |
| Subject |
Natural language processing (Computer science)
|
|
Natural language processing (Computer science)
|
| Form |
Electronic book
|
| Author |
Gupta, Amit Kumar
|
|
Prasad, Rajesh
|
| ISBN |
9781000789508 |
|
1000789500 |
|
